Navegando por Assunto "Desmatamento - Amazônia"

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Acesso aberto (Open Access)
Impactos de mudanças climáticas e desmatamento na distribuição geográfica de Cebus kaapori (Primates: Cebidae) na Amazônia
(Universidade Federal do Pará, 2018-04-02) GOMES, Letícia Braga; FREDERICO, Renata Guimarães; http://lattes.cnpq.br/3156181119549976; OLIVEIRA, Ana Cristina Mendes de; http://lattes.cnpq.br/1199691414821581
Climate change and deforestation are among the greatest threats to biodiversity. In the Amazon, the establishment of Protected Areas is an important tool to reduce the negative impacts of these threats, favoring the protection of biodiversity. Amazonia holds the largest number of primates in the world. Primates are highly sensitive to forest loss and habitat modification, which directly threatens the survival of their populations. The Ka’apor Capuchin Cebus kaapori is considered the rarest and most threatened primate species in the Amazon, and is classified as Critically Endangered by the International Union for the Conservation of Nature. Here, we evaluated the impacts of climate change and deforestation on Cebus kaapori distribution area. We modeled the distribution of the species under current and future (2050) climates and overlapped these models with established protect areas as well as current forest cover and that expected for 2050 in two different economic scenarios acoording to a land-use model. We found that climate change might lead to up to 97% of loss of climatic suitable area for Cebus kaapori within the next 30 years. The situation worsens when considering current forest loss and future deforestation projections, both under a governance scenario and in under the business-as-usual scenario. We show that the restricted distribution of Cebus kaapori, coupled with likely high reduction in suitable areas for species occurrence, low coverage in protected areas and fragmentation of potential adaptive areas for occurrence in the future, might reduce species’ populations to an unviable level of survival in nature.
Acesso aberto (Open Access)
Modelo de inteligência artificial para estimativa do desmatamento considerando a rede de transporte rodoviário do estado do Pará
(Universidade Federal do Pará, 2022-01-10) NEVES, Patrícia Bittencourt Tavares das; BLANCO, Claudio José Cavalcante; http://lattes.cnpq.br/8319326553139808; https://orcid.org/0000-0001-8022-2647; DUARTE, André Augusto Azevedo Montenegro; http://lattes.cnpq.br/1135221873341973; https://orcid.org/0000-0003-4586-1587
Since the decade of 1950s the Amazonian and Brazilian transportation complex prioritized the model of road transport. Past studies point that the regular roadway system that is integrated to a clandestine roadway complex is strongly related to the Amazon forest deforestation. Thus, in this work we performed a quantitative analysis of the variables related to the process of deforestation of the Amazon forest, a natural resource of great environment and economic significance, and the socioeconomic development of the region in the period between 1988 and 2018. The geographical study area is the state of Pará, located in the Oriental Amazon, the second largest state of Brazil in territorial extension and the most devastated. We used machine learning in the modeling of the quantitative variables related to the transportation infrastructure, social variables and economic variables, e.g., the devastated area. The random forest model presented the best performance with the generated function (using least squares method). It was estimated the devastated area for the years of 2020, 2030, 2040 and 2050. Sensitivity analysis was used to evaluate the devastated area after the implementation of the roads BR-163 and BR-210 in the north of Pará. The results show that given the current scenario the devastation tends to continue intensively in the next three decades, with a 25.77% increase over the current region albeit with decreasing ten-year rates of forestation loss, and the estimation of the deforested area caused by the implementation of federal roadway networks goes from 4,703.43 km2 to 6,567.48 km2 .